Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of Information Retrieval on the Semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search.

Abstract. This paper discusses problems of creating and using ontology library services in production use. One approach to a solution is presented with an online implementation—the Finnish Ontology Library Service ONKI — that is in pilot use on a national level in Finland. ONKI contributes to previous research on ontology libraries in many ways: First, mashup and web service support with various tools is provided for cost-efficient utilization of ontologies in indexing and search applications. Second, services covering the different phases of the ontology life cycle are provided. Third, the services are provided and used in real world applications on a national scale. Fourth, the ontology framework is being developed by a collaborative effort by organizations representing different application domains, such as health, culture, and business. 1

Abstract: Ontologging is an ontology-driven environment to enable next generation knowledge management applications building on Semantic Web technology. In this paper we first present the conceptual architecture underlying Ontologging. Second, we focus on two important challenges for ontology-based knowledge management, namely the supporting multiple ontologies and managing ontology evolution. We will provide a general approach for handling these two essential issues within the Ontologging architecture. Key words: Knowledge management, ontology mapping, ontology evolution. 1.

Abstract. A totally semantic measure is presented which is able to calculate a similarity value between concept descriptions and also between concept description and individual or between individuals expressed in an expressive description logic. It is applicable on symbolic descriptions although it uses a numeric approach for the calculus. Considering that Description Logics stand as the theoretic framework for the ontological knowledge representation and reasoning, the proposed measure can be effectively used for agglomerative and divisional clustering task applied to the semantic web domain. 1

Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induction, information extraction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scalability. We present OntoUSP, a system that induces and populates a probabilistic ontology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierarchy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate OntoUSP by using it to extract a knowledge base from biomedical abstracts and answer questions. OntoUSP improves on the recall of USP by 47 % and greatly outperforms previous state-of-the-art approaches. 1

We present a national ontology development and service framework being developed in Finland in 2003-2007. The framework is based on a set of related core ontologies, most notably on a national upper ontology based on the commonly used Finnish General Thesaurus YSA maintained by the National Library of Finland. The framework implements three ontology services by a web-based system ONKI. Firstly, ONKI supports distributed collaborative development and versioning of interdependent ontologies. Secondly, external cataloging and indexing systems can use ONKI as a web service for ontology-based annotations. Thirdly, information retrieval systems can use ONKI for disambiguating keyword meanings for concept-based search on the Semantic Web.

Abstract—It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable. Index Terms—Web intelligence, ontology mining, Web mining, Web user profiles. 1

We present INDUS (Intelligent Data Understanding System) , a federated, query-centric system for knowledge acquisition from autonomous, distributed, semantically heterogeneous data sources that can be viewed (conceptually) as tables. INDUS employs ontologies and inter-ontology mappings, to enable a user or an application to view a collection of such data sources (regardless of location, internal structure and query interfaces) as though they were a collection of tables structured according to an ontology supplied by the user. This allows INDUS to answer user queries against distributed, semantically heterogeneous data sources without the need for a centralized data warehouse or a common global ontology. We used INDUS framework to design algorithms for learning probabilistic models (e.g., Naive Bayes models) for predicting GO functional classification of a protein based on training sequences that are distributed among SWISSPROT and MIPS data sources. Mappings such as EC2GO and MIPS2GO were used to resolve the semantic di#erences between these data sources when answering queries posed by the learning algorithms. Our results show that INDUS can be successfully used for integrative analysis of data from multiple sources needed for collaborative discovery in computational biology.

In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMA-KA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment. 1

This article presents the vision and results of creating the basis for a national semantic web content infrastructure in Finland in 2003–2007. The main elements of the infrastructure are shared and open metadata schemas, core ontologies, and public ontology services. Several practical applications testing and demonstrating the usefulness of the infrastructure are overviewed in the fields of eCulture, eHealth, eGovernment, eLearning, and eCommerce. 1 A Semantic Content Infrastructure The Semantic Web 1 is based on a metadata layer that describes the contents and services on the web in a machine “understandable ” way based on ontologies [5, 34]. The idea from the application viewpoint is simple: if the machine understands the contents and services it is dealing with, then better interoperability of web systems can be obtained and intelligent services provided to the end-users. This papers argues that a conceptual “semantic content infrastructure ” is needed for the semantic web, in the same way as roads are needed for traffic and transportation, power plants and electrical networks are needed for energy supply, or GSM standards and networks are needed for mobile phones and wireless communication. A solid, commonly shared infrastructure would make it much easier and cheaper for public organizations and companies to create interoperable, intelligent services on the coming semantic web. In our view, the infrastructure should be open source and its central components be maintained by the public sector in order to guarantee wide usage and interoperability across different application domains and user communities. 1